11 citations · 17 across the 5 of their papers we have counts for
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cs.IR2026
Rethinking Multi-objective Ranking Ensemble in Recommender System: From Score Fusion to Rank Consistency
Boyang Xia, Zhou Yu, Zhiliang Zhu +5
The industrial recommender systems always pursue more than one business goals. The inherent intensions between objectives pose significant challenges for ranking stage. A popular s…
cs.IR2019★ 1 cited
Privileged Features Distillation at Taobao Recommendations
Chen Xu, Quan Li, Junfeng Ge +7
Features play an important role in the prediction tasks of e-commerce recommendations. To guarantee the consistency of off-line training and on-line serving, we usually utilize the…
cs.IR2019★ 11 cited
Personalized Re-ranking for Recommendation
Changhua Pei, Yi Zhang, Yongfeng Zhang +6
Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optim…